EB902 : Machine learning applications in medical devices and data
Topic outline
-
Machine Learning methods used in classification of medical data
this course contains:
- Chapter 1 Principles of Classification
- Chapter 2 Machine Learning Methods
- Chapter 3 Practical implementation
-
Classification is a cornerstone of machine learning and data science, designed to assign categories to data instances based on their characteristics. Its importance spans domains, including image recognition, speech processing, and medical diagnosis.
Specifically, in the biomedical field, classification supports tasks such as disease detection and medical device management.
This chapter delves into the principles underlying classification, focusing on its application to medical data and devices, where challenges like data imbalance, noise, and interpretability are very important.
-
Machine Learning (ML) involves designing algorithms that allow computers to learn patterns from data and make predictions or decisions without explicit programming.
This chapter present a concise overview of key ML methods commonly used in practice.
-
This course introduces practical machine learning techniques using Python. It provides stepby-step guidance for implementing key ML methods with commonly used libraries like scikit-learn, TensorFlow, and pandas.
-
-
-
-
-
-
-
-
-